Artificial Intelligence (AI) based image analysis has an immense potential to support diagnostic histopathology, including cancer diagnostics. However, developing supervised AI methods requires large-scale annotated datasets. A potentially powerful solution is to augment training data with synthetic data. Latent diffusion models, which can generate high-quality, diverse synthetic images, are promising. However, the most common implementations rely on detailed textual descriptions, which are not generally available in this domain. This work proposes a method that constructs structured textual prompts from automatically extracted image features. We experiment with the PCam dataset, composed of tissue patches only loosely annotated as healthy or cancerous. We show that including image-derived features in the prompt, as opposed to only healthy and cancerous labels, improves the Fr\'echet Inception Distance (FID) from 178.8 to 90.2. We also show that pathologists find it challenging to detect synthetic images, with a median sensitivity/specificity of 0.55/0.55. Finally, we show that synthetic data effectively trains AI models.

翻译：基于人工智能（AI）的图像分析在支持诊断组织病理学（包括癌症诊断）方面具有巨大潜力。然而，开发监督式AI方法需要大规模标注数据集。一个潜在的有效解决方案是利用合成数据扩充训练数据。能够生成高质量、多样化合成图像的潜扩散模型前景广阔，但其最常见的实现依赖于该领域通常无法提供的详细文本描述。本研究提出一种方法，从自动提取的图像特征中构建结构化文本提示。我们在PCam数据集上进行实验，该数据集由仅标注为健康或癌变组织的松散标注组织切片组成。结果表明，与仅使用健康/癌变标签相比，在提示中加入图像衍生特征可将弗雷歇初始距离（FID）从178.8优化至90.2。我们还发现病理学家难以检测合成图像，其中位敏感度/特异性为0.55/0.55。最后，我们证明合成数据可有效训练AI模型。